Deep multiagent reinforcement learning: Challenges and directions
This paper surveys the field of deep multiagent reinforcement learning (RL). The
combination of deep neural networks with RL has gained increased traction in recent years …
combination of deep neural networks with RL has gained increased traction in recent years …
Value-decomposition networks for cooperative multi-agent learning
P Sunehag, G Lever, A Gruslys, WM Czarnecki… - ar** in multiagent reinforcement learning for self-organizing systems in assembly tasks
Self-organizing systems feature flexibility and robustness for tasks that may endure changes
over time. Various methods, eg, applying task-field and social-field, have been proposed to …
over time. Various methods, eg, applying task-field and social-field, have been proposed to …
Deep multi-agent reinforcement learning
J Foerster - 2018 - ora.ox.ac.uk
A plethora of real world problems, such as the control of autonomous vehicles and drones,
packet delivery, and many others consists of a number of agents that need to take actions …
packet delivery, and many others consists of a number of agents that need to take actions …
Multi-agent reinforcement learning guided by signal temporal logic specifications
There has been growing interest in deep reinforcement learning (DRL) algorithm design,
and reward design is one key component of DRL. Among the various techniques, formal …
and reward design is one key component of DRL. Among the various techniques, formal …